NamedCurves：基于颜色命名的学习型图像增强方法
NamedCurves: Learned Image Enhancement via Color Naming
AI总结：
受编辑工具按颜色名称调色的思路启发，提出基于学习的图像增强方法NamedCurves，通过色调曲线调整各命名颜色并经注意力融合模拟空间编辑，在两个基准数据集上性能优于同类方法。
摘要：
图像增强领域的一种主流方法是利用由原始输入图像与编辑增强版本组成的训练图像对，学习专业照片编辑器的风格。在进行图像编辑时，许多编辑工具都提供一项功能，允许用户对有限的一组常见颜色进行调整。通过颜色名称进行编辑，可以轻松调整天空的“蓝色”或树木的“绿色”等元素。受这种颜色操作方法的启发，我们提出了NamedCurves——一种基于学习的图像增强技术，它将图像拆分为少量命名颜色集合。我们的方法通过色调曲线学习针对每种特定命名颜色对图像进行全局调整，再利用基于注意力的融合机制将这些图像组合，以模拟空间编辑效果。我们在知名的Adobe 5K数据集和PPR10K数据集上，将所提方法与多种同类方法进行对比验证，结果表明该方法取得了显著的性能提升。
英文摘要：
A popular method for enhancing images involves learning the style of a professional photo editor using pairs of training images comprised of the original input with the editor-enhanced version. When manipulating images, many editing tools offer a feature that allows the user to manipulate a limited selection of familiar colors. Editing by color name allows easy adjustment of elements like the "blue" of the sky or the "green" of trees. Inspired by this approach to color manipulation, we propose NamedCurves, a learning-based image enhancement technique that separates the image into a small set of named colors. Our method learns to globally adjust the image for each specific named color via tone curves and then combines the images using an attention-based fusion mechanism to mimic spatial editing. We demonstrate the effectiveness of our method against several competing methods on the well-known Adobe 5K dataset and the PPR10K dataset, showing notable improvements.
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